In the past decade there has been increasing interest in using functional neuroimaging (especially fMRI) to uncover the intrinsic, functional organization of the human brain. This is often accomplished by collecting data while participants lie quietly in MRI scanners (commonly referred to as resting-state brain scanning). As with all new techniques and procedures, there is considerable debate on how best to analyze these data in order to provide the most valid view of the brain's functional architecture. During this past year, we have focused considerable effort on these methodological issues by completing a series of investigations aimed at identifying and ridding fMRI of sources of noise known to distort and corrupt the data, with emphasis on the role of respiration and associated motion (Power et al., PNAS,2018; Power, Lynch, Silver et al., NeuroImage, 2019; Power Lynch, Gilmore et al., PNAS, 2019). These investigations have yielded important findings on the most promising procedures for collecting and analyzing resting-state data to provide the best possible view of the human brain's intrinsic functional organization. In a series of studies, we have utilized both resting-state scanning and specific cognitive tasks to identify and evaluate the neural networks involved in different aspects of memory. One aspect of memory that we have been particularly interested in is our ability to recognize others by their facial features. Although this ability is at the core of human social interaction, it varies widely within the general population, ranging from developmental prosopagnosia (individuals with exceedingly poor memory for faces) to super-recognizers. Previous work has focused mainly on the contribution of the well described face processing network to this variability. However, given the nature of face memory in everyday life, and the social context in which it takes place, we were interested in exploring how the collaboration between different networks outside the face network affects face memory performance. Our data revealed that although the regions comprising the face-processing network were tightly coupled at rest, the strength of these connections did not predict face memory performance. Instead, face memory was dependent on multiple connections between these face processing regions and regions of the medial temporal lobe memory system (including the hippocampus), and the social processing system. Moreover, activity within these interacting networks was selective for memory for faces and did not predict memory for other visual objects (cars). These findings suggest that in the general population, variability in face memory is dependent on how well the face processing system interacts with other networks involved in memory and social processes. In another study we focused our attention on the role of a specific neural system, known as the parietal memory system, in recognizing previously encountered stimuli or events. An outstanding question in the field is whether this system responds automatically to any previously encountered information, regardless of whether we are consciously trying to remember it, or only when we need to engage in conscious recollection. Using fMRI, we evaluated this issue by measuring neural activity during tasks that either did, or did not, require conscious reflection on whether the material (pictures of common objects) had been previously seen (Gilmore et al., Neuropsychologia, 2019). Our findings indicated that the brain regions that comprise the parietal memory system respond to any previously presented stimulus, regardless of current task context. These findings suggest that regions of parietal cortex play a prominent role in automatically detecting the reoccurrence of an event or stimulus, regardless of whether we are explicitly trying to determine if it had been experienced before.